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The European Monetary Union (EMU) is the only Union that allows its members to conduct their own fiscal policy, which has to be consistent with the Maastricht treaty. This paper attempts to shed light on business cycles determinants focusing on fiscal variables in EU economies, in the time period 1996-2013, using quarterly data fully capturing the on-going recession. In this context, it also acknowledges the significant role of the Quality of Institutions and of the Elections in a Political Business cycles framework. Additionally, based on the business cycles characteristics of the EU economies it explores the potential formation of clusters in the EU economy. To this end, a number of relevant econometric techniques are employed such as: HP filtering, LLC tests; Ljung-Box tests; Fourier analysis; Rolling windows; Dynamic Panel Data analysis; Toda-Yamamoto causality test, Panel Seemingly Unrelated Regressions (SUR) and k-means clustering. Our findings suggest that Social Benefits, Social Transfers and Gross Debt are the most significant policy variables with a counter-cyclical character, while taxation was found to have a destabilizing effect. In addition, Elections and the Quality of Institutions were found to significantly affect the key fiscal variables examined. Meanwhile, most peripheral countries lie in one cluster suggesting that the recent crisis has led a number of small(-er) peripheral economies to cluster together. [ABSTRACT FROM AUTHOR]

Abstract: The FLASH project was implemented from 2006 to 2010 under the EU FP6 framework. The project focused on using lightning observations to better understand and predict convective storms that result in flash floods. As part of the project 23 case studies of flash floods in the Mediterranean region were examined. For the analysis of these storms lightning data from the ZEUS network were used together with satellite derived rainfall estimates in order to understand the storm development and electrification. In addition, these case studies were simulated using mesoscale meteorological models to better understand the meteorological and synoptic conditions leading up to these intense storms. As part of this project tools for short term predictions (nowcasts) of intense convection across the Mediterranean and Europe, and long term forecasts (a few days) of the likelihood of intense convection were developed. The project also focused on educational outreach through our website http://flashproject.org supplying real time lightning observations, real time experimental nowcasts, forecasts and educational materials. While flash floods and intense thunderstorms cannot be prevented as the climate changes, long-range regional lightning networks can supply valuable data, in real time, for warning end-users and stakeholders of imminent intense rainfall and possible flash floods. [Copyright &y& Elsevier]

The article discusses the role of the Jewish community in the economic and cultural history of Salonica (Thessaloniki), Greece, from the time of Ottoman rule into the 20th century. The city's history as an important center of commerce and multiculturalism is discussed, including the migration of Sephardic and Ashkenazi Jews to the city starting in the late 15th century. The impact of Jewish entrepreneurship in the areas of trade, publishing, education, industry, and politics starting in the 19th century are then discussed.

This paper describes the first comparison performed in Europe between the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) and a (C-band) ground-based radar. The scope of the research is within the research activities of VOLTAIRE, a European Commission project. Notwithstanding the time lag and differences in the frequency of operation, sample volume. geometrical viewing angles, attenuation and radar sensitivity, there Is a reasonable agreement between the two sensors (∼ 1-2 dBZ). Ground-based and space-borne radar measurements provide a complementary view of such a complex phenomenon like We precipitation field. [ABSTRACT FROM AUTHOR]

PROTHEGO (PROTection of European Cultural HEritage from GeO-hazards) uses novel space technology to monitor surface deformation with mm precision to analyze the impact of geohazards in cultural heritage sites in Europe. The project includes the 395 monuments of UNESCO in Europe to monitor geo-hazards, with case studies conducted in 4 UNESCO sites in England, Spain, Italy and Cyprus. The PROTHEGO project uses long-term low-impact monitoring systems, such as UAVs and geodetic techniques, as well as InSAR data to monitor and assess the risk from natural hazards on the archaeological site to evaluate potential geo-hazards. This paper will present an overview of best practices for the innovative diagnosis, monitoring, mitigation and preservation of Cultural Heritage monuments sites affected by geo-hazards that are potentially unstable due to landslides, sinkholes, settlement, subsidence, active tectonics as well as structural deformation. [ABSTRACT FROM AUTHOR]

Rainfall erosivity as a dynamic factor of soil loss by water erosion is modelled intra-annually for the first time at European scale. The development of Rainfall Erosivity Database at European Scale (REDES) and its 2015 update with the extension to monthly component allowed to develop monthly and seasonal R-factor maps and assess rainfall erosivity both spatially and temporally. During winter months, significant rainfall erosivity is present only in part of the Mediterranean countries. A sudden increase of erosivity occurs in major part of European Union (except Mediterranean basin, western part of Britain and Ireland) in May and the highest values are registered during summer months. Starting from September, R-factor has a decreasing trend. The mean rainfall erosivity in summer is almost 4 times higher (315 MJ mm ha − 1 h − 1 ) compared to winter (87 MJ mm ha − 1 h − 1 ). The Cubist model has been selected among various statistical models to perform the spatial interpolation due to its excellent performance, ability to model non-linearity and interpretability. The monthly prediction is an order more difficult than the annual one as it is limited by the number of covariates and, for consistency, the sum of all months has to be close to annual erosivity. The performance of the Cubist models proved to be generally high, resulting in R 2 values between 0.40 and 0.64 in cross-validation. The obtained months show an increasing trend of erosivity occurring from winter to summer starting from western to Eastern Europe. The maps also show a clear delineation of areas with different erosivity seasonal patterns, whose spatial outline was evidenced by cluster analysis. The monthly erosivity maps can be used to develop composite indicators that map both intra-annual variability and concentration of erosive events. Consequently, spatio-temporal mapping of rainfall erosivity permits to identify the months and the areas with highest risk of soil loss where conservation measures should be applied in different seasons of the year. [ABSTRACT FROM AUTHOR]

Recently, in the Auerswald et al. (2015) comment on “Rainfall erosivity in Europe”, 5 criticisms were addressed: i) the neglect of seasonal erosion indices, ii) the neglect of published studies and data, iii) the low temporal resolution of the data, especially of the maximum rain intensity, iv) the use of precipitation data instead of rain data and the subsequent deviation of the R-factor in Germany and Austria compared with previous studies, and v) the differences in considered time periods between countries. We reply as follows: (i) An evaluation of the seasonal erosion index at the European scale is, to our knowledge, not achievable at present with the available data but would be a future goal. Synchronous publication of the seasonal erosion index is not mandatory, specifically because seasonal soil loss ratios are not available at this scale to date. We are looking forward to the appropriate study by the authors of the comment, who assert that they have access to the required data. (ii) We discuss and evaluate relevant studies in our original work and in this reply; however, we cannot consider what is not available to the scientific community. (iii) The third point of critique was based on a misunderstanding by Auerswald et al. (2015), as we did indeed calculate the maximum intensity with the highest resolution of data available. (iv) The low R-factor values in Germany and the higher values in Austria compared with previous studies are not due to the involvement of snow but are rather due to a Pan-European interpolation. We argue that an interpolation across the borders of Austria creates a more reliable data set. (v) We agree that the use of a short time series or time series from different periods is generally a problem in all large-scale studies and requires improvement in the future. However, because this affects countries with a rather low variability of the R-factor in our study, we are confident that the overall results of the map are not biased. In conclusion, the Pan-European rainfall data compilation (REDES) was a great success and yielded data from 1541 stations with an average length of 17.1 years and a temporal resolution of < 60 min. However, a Pan-European data collection will never be complete without the help and supply of data from its users. Thus, we invite the authors of the comment to share their data in the open REDES to help build even better rainfall-erosivity maps at regional or European scales. [ABSTRACT FROM AUTHOR]

Rainfall is one the main drivers of soil erosion. The erosive force of rainfall is expressed as rainfall erosivity. Rainfall erosivity considers the rainfall amount and intensity, and is most commonly expressed as the R-factor in the USLE model and its revised version, RUSLE. At national and continental levels, the scarce availability of data obliges soil erosion modellers to estimate this factor based on rainfall data with only low temporal resolution (daily, monthly, annual averages). The purpose of this study is to assess rainfall erosivity in Europe in the form of the RUSLE R-factor, based on the best available datasets. Data have been collected from 1541 precipitation stations in all European Union (EU) Member States and Switzerland, with temporal resolutions of 5 to 60 min. The R-factor values calculated from precipitation data of different temporal resolutions were normalised to R-factor values with temporal resolutions of 30 min using linear regression functions. Precipitation time series ranged from a minimum of 5 years to a maximum of 40 years. The average time series per precipitation station is around 17.1 years, the most datasets including the first decade of the 21st century. Gaussian Process Regression (GPR) has been used to interpolate the R-factor station values to a European rainfall erosivity map at 1 km resolution. The covariates used for the R-factor interpolation were climatic data (total precipitation, seasonal precipitation, precipitation of driest/wettest months, average temperature), elevation and latitude/longitude. The mean R-factor for the EU plus Switzerland is 722 MJ mm ha − 1 h − 1 yr − 1 , with the highest values (> 1000 MJ mm ha − 1 h − 1 yr − 1 ) in the Mediterranean and alpine regions and the lowest (< 500 MJ mm ha − 1 h − 1 yr − 1 ) in the Nordic countries. The erosivity density (erosivity normalised to annual precipitation amounts) was also the highest in Mediterranean regions which implies high risk for erosive events and floods. [ABSTRACT FROM AUTHOR]

Official reports and anecdotal evidence suggest that people with power frequently put pressure on athletes to fix a match. Therefore, it is assumed that athletes may attribute their involvement to this pressure. The present study was designed to investigate the role that power, attributions and moral emotions may play in the decision to fix a match. Team and individual sport athletes (N = 427) competing in five European countries participated in a quasi-experimental vignette design. Participants completed eight vignettes manipulating power, source of attributions and stability of attributions. Match-fixing susceptibility and five discrete anticipated moral emotions (guilt, shame, pride, indifference, anger) were measured. The results of the analyses demonstrated that athletes are perceived to be most susceptible to match-fixing when the reason is related to a stable attribute of the individual (e.g., enjoying gambling, having a betting problem). However, participants reported also being susceptible to match-fixing when power is high. Anticipated emotions negatively predicted match-fixing susceptibility and mediated the effect of attributions and power on match-fixing susceptibility. The findings provide information on the interplay between attributions, power and anticipated emotions in predicting match-fixing susceptibility, and the determinants of match-fixing susceptibility. This will be of benefit to policy makers, sporting organizations and researchers in developing policies and interventions to protect athletes from being vulnerable to match-fixing requests. • Athletes are perceived to be most susceptible to match-fixing when related to a stable attribute of the individual (e.g., enjoying gambling). • However, athletes are also susceptible to match-fixing when they receive a request from a person in a position of power. • Anticipating negative emotions one may experience if one engages in match-fixing is a protective factor. [ABSTRACT FROM AUTHOR]